Performance evaluation of data compression transforms for underwater imaging and object recognition

Underwater (UW) imagery presents several challenging problems for automated target recognition (ATR) using compressed imagery, due to the presence of noise, point-spread function effects resulting from camera or media inhomogeneities, as well as loss of contrast and resolution due to in-water scattering and absorption. In practice, sensor noise can severely degrade algorithm performance by producing featural aliasing in the reconstructed (decompressed) imagery. This paper summarizes the latest research in low-distortion, high-rate image compression transforms for ATR applications that require image transmission along low-bandwidth channels such as UW acoustic uplinks. In particular, a novel transform called BLAST has been developed that can achieve compression ratios in the range 50:1<CR<280:1 on UW imagery at visually acceptable quality, via simple arithmetic operations over small local neighborhoods. Comparative analysis of performance among BLAST, pyramid coding (EPIC), and visual pattern image coding (VPIC) includes compression ratio, information loss, and computational efficiency measured over a large database of UW imagery. Information loss is discussed in terms of the modulation transfer function and several image quality measures. Parallel implementation of the BLAST, VPIC and EPIC transforms is discussed in terms of speed advantages and storage costs